Update app.py

app.py

@@ -2,44 +2,47 @@ import gradio as gr
 from diffusers import StableDiffusionImg2ImgPipeline
 import torch
 
-pipe = StableDiffusionImg2ImgPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
-pipe = pipe.to("cuda")
+# فقط CPU
+pipe = StableDiffusionImg2ImgPipeline.from_pretrained(
+    "runwayml/stable-diffusion-v1-5",
+    torch_dtype=torch.float32,  # float16 روی CPU نمی‌شه
+    safety_checker=None,
+    requires_safety_checker=False
+)
 
-def generate(image, prompt, negative_prompt="", steps=45, strength=0.25, cfg=5.0, lora_path=None, lora_strength=0.5):
-    if lora_path:
-        pipe.load_lora_weights(lora_path)
-        pipe.fuse_lora(lora_strength=lora_strength)
-    
-    result = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        image=image,
-        num_inference_steps=steps,
-        strength=strength,
-        guidance_scale=cfg
-    ).images[0]
-    
-    if lora_path:
-        pipe.unfuse_lora()
-    
-    return result
+def generate(image, prompt, negative_prompt="", steps=20, strength=0.3, cfg=7.0, lora_path=None, lora_strength=0.6):
+    try:
+        if lora_path:
+            pipe.load_lora_weights(lora_path)
+        
+        result = pipe(
+            prompt=prompt,
+            negative_prompt=negative_prompt,
+            image=image,
+            num_inference_steps=steps,
+            strength=strength,
+            guidance_scale=cfg
+        ).images[0]
+        
+        return result
+    except Exception as e:
+        return f"Error: {str(e)}"
 
 with gr.Blocks() as demo:
-    gr.Markdown("## NSFW Image-to-Image with Grool LoRA")
+    gr.Markdown("## NSFW Image-to-Image (CPU Only)")
     with gr.Row():
         with gr.Column():
             input_img = gr.Image(type="pil", label="Upload Face Photo")
-            prompt = gr.Textbox(label="Prompt", lines=4, value="photorealistic 8k, nude girl sitting on bed in bright bedroom, legs open, wearing ultra-narrow lace micro-thong with transparent mesh, small delicate pink vulva and anus completely visible through lace, tiny entrance, petite labia, wetness, face locked to input image --ar 2:3")
-            neg_prompt = gr.Textbox(label="Negative", value="large vulva, thick lines, deformed, plastic, child")
-            steps = gr.Slider(20, 100, 45)
-            strength = gr.Slider(0, 1, 0.25, label="Denoising Strength")
-            cfg = gr.Slider(1, 20, 5.0)
+            prompt = gr.Textbox(label="Prompt", lines=3, value="nude girl in bedroom, wearing tiny lace thong, small vulva visible, face locked to input image")
+            neg_prompt = gr.Textbox(label="Negative", value="deformed, large vulva, child")
+            steps = gr.Slider(10, 30, 20, label="Steps (کم = سریع‌تر)")
+            strength = gr.Slider(0.1, 0.5, 0.3, label="Strength")
+            cfg = gr.Slider(5, 10, 7.0, label="CFG")
             lora_file = gr.File(label="Upload LoRA")
-            lora_strength = gr.Slider(0, 1, 0.5)
-            btn = gr.Button("Generate")
+            btn = gr.Button("Generate (30–60s)")
         with gr.Column():
             output = gr.Image(label="Result")
     
-    btn.click(generate, [input_img, prompt, neg_prompt, steps, strength, cfg, lora_file, lora_strength], output)
+    btn.click(generate, [input_img, prompt, neg_prompt, steps, strength, cfg, lora_file], output)
 
 demo.launch()